There has been a recent increasing trend in fraudulent insurance claims. To detect fraudulent claims insurers use statistical predictive modeling. However, the fraudulent-claim data are complex, and the current standard regression methods are not powerful enough to uncover the “knotty” interrelationships among the fraudulent-claim data. The model builder needs a cutting-edge data mining technique to extract the quintessence of fraudulent-claim data to insure an accurate classification fraudulent claims model can be built to produce fraud propensity scores, which the insurance adjuster would then use to target the criminal claimants.